Object and pattern recognition involves the goal of recognizing patterns and objects from large quantities of data and also the ability to estimate the positions and orientations of recognized patterns and/or objects in three-dimensional space. The data to be analyzed can be, for example, two-dimensional gray-scale or color images or three-dimensional range data images, depending upon specific design goals. Applications for object recognition are many and include industrial machine vision, medical image analysis, and speech recognition.
One of the problems with current object and/or pattern recognition techniques is that such methodologies are limited primarily to software-based implementations. A number of software-based object recognition methods have been developed. Because software implementations are performed on conventional sequential computers, however, they are unable to take advantage of the inherent parallelism of recognition algorithms. That is, object and/or pattern recognition can be enhanced substantially if based on parallel processing configurations rather than sequential methods.
Hardware accelerators do exist for performing parallel pattern matching. Such systems have become known as a Content Addressable Memory (CAM). Content Addressable Memories (CAM's) perform simultaneous matching of many patterns by associating comparison circuitry with stored data and greatly improve the performance of a number of applications. Unfortunately, current CAM's do not completely solve pattern recognition problems and their results must be analyzed further by serial processing methods. We are left with a system that possess great potential but substantially lacks at performing the calculations required for massive pattern recognition tasks, particularly for the field of intelligent signal processing.
As recognition systems grow to encompass many classifications, the size of the computation increases dramatically. For example, consider the case of a pattern recognition system designed to recognize 10 patterns and another system design to recognize 100 patterns. Although the later recognition device will classify 10 times the number of patterns it will consume 10 times the resources. If the system is emulated in a serial device it will take 10 times as long. Yet, it is usually the case that the recognition of each pattern is not dependant on the recognition of any other pattern. If the recognition system is constructed in an entirely parallel arrangement, so that each pattern can be analyzed independently, then as the pattern recognition system grows larger the time for recognition remains the same. For these types of highly parallel recognition problems it would be advantageous to design a completely parallel architecture capable of infinite scalability while utilizing current fabrication technology.
Applications drive the need for computational power. Many of the current unsolved problems with commercial applicability require real-time signal processing of massive data streams. Image and speech recognition are two important examples. Object recognition is intrinsically solved in most moderately complex biological nervous systems in 100 synaptic steps or less. Almost without exception, current technology fails at performing tasks most animals and insects find effortless. Actions such as walking, flying or driving require prodigious amounts of computation and analysis on vast quantities of high-dimensional noisy data. With current technology it is relatively easy to obtain massive data streams. It is not easy, however, to effectively act on this data. The ability to distinguish objects in large quantities of high-dimensional streaming information is absolutely critical, and currently represents a serious computational bottleneck. A robust and scalable solution to this problem would make possible vastly more intelligent computer systems.
It is believed that a possible solution to the aforementioned problems may involve the use of nanotechnology-based devices and implementations. One example of a hardware device implemented with nanotechnology-based components is the physical neural network disclosed in U.S. Pat. No. 6,889,216, entitled “Physical Neural Network Design Incorporating Nanotechnology,” which issued to Alex Nugent on May 3, 2005. U.S. Pat. No. 6,889,216 is incorporated herein by reference in its entirety. Such a physical neural network, which can be referred to as a Knowm™ network generally includes one or more neuron-like nodes, which are formed from a plurality of interconnected nanoconnections formed from nanoconductors. Such connections constitute Knowm™ connections. Each neuron-like node sums one or more input signals and generates one or more output signals based on a threshold associated with the input signal.
The Knowm™ device physical neural network also includes a connection network formed from the interconnected nanoconnections, such that the interconnected nanoconnections used thereof by one or more of the neuron-like nodes are strengthened or weakened according to an application of an electric field, variations in frequency, and so forth. U.S. Pat. No. 6,889,216 is incorporated herein by reference.
Another example of a Knowm™ network or system is described in U.S. Patent Publication No. 20030236760 (abandoned), entitled “Multi-layer Training in a Physical Neural Network Formed Utilizing Nanotechnology,” by inventor Alex Nugent, which was published on Dec. 25, 2003. U.S. Patent Publication No. 20030236760 (abandoned) generally describes methods and systems for training at least one connection network located between neuron layers within a multi-layer physical neural network (e.g., a Knowm™ network or device). The multi-layer physical neural network described in U.S. Patent Publication No. 20030236760 (abandoned) can be formed with nanotechnology-based components in the context of a plurality of inputs and a plurality outputs. Such a multi-layer physical neural network is composed of a plurality of layers, wherein each layer includes one or more connection networks and one or more associated neurons, which are configured with nanotechnology-based components.
Thereafter, a training wave, as further described in U.S. Patent Publication No. 20030236760 (abandoned), can be initiated across one or more connection networks associated with an initial layer of the multi-layer physical neural network which propagates thereafter through succeeding connection networks of succeeding layers of the multi-layer physical neural network by successively closing and opening at least one switch associated with each layer of the multi-layer physical neural network. At least one feedback signal thereof can be automatically provided to each preceding connection network associated with each preceding layer thereof to strengthen or weaken nanoconnections associated with each connection network of the multi-layer physical neural network. U.S. Patent Publication No. 20030236760 is incorporated herein by reference.
A further example of a Knowm™ network or system is described in U.S. Patent Publication No. 20040039717, entitled High-density synapse chip using nanoparticles” by inventor Alex Nugent. U.S. Patent Publication No. 20040039717 published on Feb. 26, 2004 and generally describes a physical neural network synapse chip (i.e., a Knowm™ chip) and a method for forming such a synapse chip. The synapse or Knowm™ chip can be configured to include an input layer comprising a plurality of input electrodes and an output layer comprising a plurality of output electrodes, such that the output electrodes are located perpendicular to the input electrodes. A gap is generally formed between the input layer and the output layer.
A solution can then be provided which is prepared from a plurality of nanoconductors and a dielectric solvent. The solution is located within the gap, such that an electric field is applied across the gap from the input layer to the output layer to form nanoconnections of a physical neural network implemented by the synapse chip. Such a gap can thus be configured as an electrode gap. The input electrodes can be configured as an array of input electrodes, while the output electrodes can be configured as an array of output electrodes. U.S. Patent Publication No. 20040039717 is also incorporated herein by reference.
A further example of a Knowm™ network or system is disclosed in U.S. Patent Publication No. 20040153426, entitled “Physical Neural Network Liquid State Machine Utilizing Nanotechnology,” by inventor Alex Nugent, which was published on Aug. 5, 2004. U.S. Patent Publication No. 20040153426 generally discloses a physical neural network (i.e., a Knowm™ network), which functions as a liquid state machine.
The physical neural network described in U.S. Patent Publication No. 20040153426 can be configured from molecular connections located within a dielectric solvent between pre-synaptic and post-synaptic electrodes thereof, such that the molecular connections are strengthened or weakened according to an application of an electric field or a frequency thereof to provide physical neural network connections thereof. A supervised learning mechanism is associated with the liquid state machine, whereby connections strengths of the molecular connections are determined by pre-synaptic and post-synaptic activity respectively associated with the pre-synaptic and post-synaptic electrodes, wherein the liquid state machine comprises a dynamic fading memory mechanism. U.S. Patent Publication No. 20040153426 is also incorporated herein by reference.
A further example of a Knowm™ network or system is disclosed in U.S. Patent Publication No. 20040162796, entitled “Application of Hebbian and anti-Hebbian Learning to Nanotechnology-based Physical Neural Networks” by inventor Alex Nugent, which published on Aug. 19, 2004. U.S. Patent Publication No. 20040162796 generally discloses a physical neural network (i.e., Knowm™ network) configured utilizing nanotechnology. The Knowm™ network disclosed in U.S. Patent Publication No. 20040162796 includes a plurality of molecular conductors (e.g., nanoconductors) which form neural connections between pre-synaptic and post-synaptic components of the physical neural network.
Based on the foregoing, it is believed that a need exists to perform massively parallel object recognition. In particular, a need exists for performing a probabilistic best match of a pattern from an unlimited size database in a fixed time interval. It is a believed that a solution to this need involves the use of nanotechnology components and systems as disclosed in greater detail herein.